When a model is trained using a specific dataset with limited diversity in labels, it may accurately predict labels for objects within that dataset. However, when applied to real-time recognition tasks using a webcam, the model might incorrectly predict labels for objects not present in the training data. This poses a challenge as the model's predictions may not align with the variety of objects encountered in real-world scenarios.
- Example: I trained a real-time recognition model for a webcam, where I have classes lc = {a, b, c, ..., m}. The model consistently predicts class lc perfectly. However, when I input a class that doesn't belong to lc, it still predicts something from class lc.
Are there any solutions or opinions that experts can share to guide me further in improving the model?
Thank you for considering your opinion on my problems.
A major challenge faced in real-time computer vision recognition is detecting objects that are not included in the training dataset. This can result in misclassification or complete failure to differentiate between recognised objects. One way is to use transfer learning techniques, which involve pre-trained models and fine-tuning them on a small dataset of new objects. Furthermore, data augmentation techniques can help vary the training dataset, thereby boosting the model's capacity to generalize to new items. Continuous model changes based on user feedback, along with online learning methods, can improve the system's adaptability to novel items in real-time scenarios.
I agree with you, Dr. Adil Hussain. It works in some cases. But what if, after doing all of these, the issue persists? Is there anything that can be done during deployment? So that the model only predicts what it knows, and for other unknown classes, the model remains neutral?
Bayesian neural networks (BNN) may be appropriate in this case, as they allow dealing with uncertainties in neural networks, which can be beneficial for improving model generalization, especially when dealing with unseen classes. the advantage of these networks is that they allow determining the uncertainty of the responses, determining for example when the response obtained was achieved by an extrapolation of the data, rather than an interpolation of them.
Some of the solutions are transfer learning, data augmentation, one-shot learning, ensemble learning, active learning, and continuous learning.
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